EXTRACTION OF ROAD NETWORKS USING PAN-SHARPENED MULTISPECTRAL AND PANCHROMATIC QUICKBIRD IMAGES
Bibliographic record
Abstract
Les methodes d'extraction routiere basees sur la classification multispectrale traditionnelle separent les routes des autres caracteristiques du sol selon les caracteristiques spectrales des pixels individuels. Pour faire usage des proprietes spatiales d'images satellitaires haute resolution, dans cet article, nous integrons l'information spectrale d'une image multispectrale a l'information spatiale d'une image panchromatique pour l'extraction routiere par une technique d'affinage global et un algorithme de reclassification basee sur les contours. Premierement, l'image multispectrale a faible resolution est fusionnee a l'image panchromatique a haute resolution. Ensuite, l'image affinee est classifiee pour determiner la classe de routes qui peut comprendre des objets non routiers. Les routes classifiees sont ensuite segmentees et reclassifiees a l'aide de l'information de la texture directionnelle de l'image des routes classifiees et de l'information sur les contours de l'image panchromatique. En utilisant la texture, le contour, la forme et la dimension, les objets non routiers, par exemple les petites entrees, les toitures des maisons et les parcs de stationnement peuvent etre enleves efficacement. Les evaluations de la qualite en milieu urbain montrent que l'integralite et la justesse des principales routes extraites quant a leur longueur sont meilleures que 90 % et 97 %, respectivement.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".